1. Technical Field
The present invention relates to a system and method for detecting a protrusion in a medical image and, more particularly, to detecting a protrusion in a medical image by calculating a distance map of a segmented medical image and processing gradient characteristics of the distance mapped medical image to detect a protrusion in the medical image.
2. Discussion of the Related Art
In the field of medical imaging, various systems have been developed for generating medical images of various anatomical structures of individuals for the purposes of screening and evaluating medical conditions. These imaging systems include, for example, computed tomography (CT) imaging, magnetic resonance imaging (MRI), positron emission tomography (PET), etc. Each imaging modality may provide unique advantages over other modalities for screening and evaluating certain types of diseases, medical conditions or anatomical abnormalities, including, for example, colonic polyps, aneurysms, lung nodules, calcification on heart or artery tissue, cancer micro-calcifications or masses in breast tissue, and various other lesions or abnormalities.
For example, CT imaging systems can be used to obtain a set of cross-sectional images or two-dimensional (2D) “slices” of a region or interest (ROI) of a patient for purposes of imaging organs and other anatomies. The CT modality is commonly employed for purposes of diagnosing disease because such a modality provides precise images that illustrate the size, shape, and location of various anatomical structures such as organs, soft tissues, and bones, and enables a more accurate evaluation of lesions and abnormal anatomical structures such as cancer, polyps, etc.
One conventional method that physicians, clinicians, radiologists, etc., use for diagnosing and evaluating medical conditions is to manually review hard-copies (X-ray films, prints, photographs, etc.) of medical images that are reconstructed from an acquired dataset, to discern characteristic features of interest. For example, CT image data that is acquired during a CT examination can be used to produce a set of 2D medical images (X-ray films) that can be viewed to identify potential abnormal anatomical structures or lesions by a trained physician, clinician, radiologist, etc. A virtual colonoscopy may produce medical images that include normal anatomical structures corresponding to the colon, and a trained radiologist may be able to identify small polyps among these structures that are potentially cancerous or pre-cancerous. However, a trained physician, clinician or radiologist may overlook a medical condition such as colonic polyps due to human error.
Accordingly, various image processing systems and tools have been developed to assist physicians, clinicians, radiologists, etc. in evaluating medical images to diagnose medical conditions. For example, computer-aided detection (CAD) tools have been developed for various clinical applications to provide automated detection of medical conditions in medical images. In general, CAD systems employ methods for digital signal processing of image data (e.g., CT data) to automatically detect colonic polyps and other abnormal anatomical structures such as lung nodules, lesions, aneurysms, calcification on heart or artery tissue, micro-calcifications or masses in breast tissue, etc.
Although such CAD systems are very useful for diagnostic purposes, cost-reduction is difficult to achieve as the amount of data, for example, a radiologist, has to examine is abundant thus leading to lengthy analysis time and high costs of professional charges for the radiologist's interpretation. In addition, many CAD systems suffer from false positives (e.g., incorrectly identifying normal tissues as abnormal) and false negatives (e.g., failing to correctly identify abnormalities). Accordingly, there is a need for a CAD technique that identifies medical conditions such as colonic polyps in medical images accurately so that a medical expert such as a radiologist can efficiently and correctly analyze these conditions in a short amount of time.